55,014 research outputs found

    Single-machine scheduling with stepwise tardiness costs and release times

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    We study a scheduling problem that belongs to the yard operations component of the railroad planning problems, namely the hump sequencing problem. The scheduling problem is characterized as a single-machine problem with stepwise tardiness cost objectives. This is a new scheduling criterion which is also relevant in the context of traditional machine scheduling problems. We produce complexity results that characterize some cases of the problem as pseudo-polynomially solvable. For the difficult-to-solve cases of the problem, we develop mathematical programming formulations, and propose heuristic algorithms. We test the formulations and heuristic algorithms on randomly generated single-machine scheduling problems and real-life datasets for the hump sequencing problem. Our experiments show promising results for both sets of problems

    On the Complexity of tt-Closeness Anonymization and Related Problems

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    An important issue in releasing individual data is to protect the sensitive information from being leaked and maliciously utilized. Famous privacy preserving principles that aim to ensure both data privacy and data integrity, such as kk-anonymity and ll-diversity, have been extensively studied both theoretically and empirically. Nonetheless, these widely-adopted principles are still insufficient to prevent attribute disclosure if the attacker has partial knowledge about the overall sensitive data distribution. The tt-closeness principle has been proposed to fix this, which also has the benefit of supporting numerical sensitive attributes. However, in contrast to kk-anonymity and ll-diversity, the theoretical aspect of tt-closeness has not been well investigated. We initiate the first systematic theoretical study on the tt-closeness principle under the commonly-used attribute suppression model. We prove that for every constant tt such that 0t<10\leq t<1, it is NP-hard to find an optimal tt-closeness generalization of a given table. The proof consists of several reductions each of which works for different values of tt, which together cover the full range. To complement this negative result, we also provide exact and fixed-parameter algorithms. Finally, we answer some open questions regarding the complexity of kk-anonymity and ll-diversity left in the literature.Comment: An extended abstract to appear in DASFAA 201

    Quantum Optimization Problems

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    Krentel [J. Comput. System. Sci., 36, pp.490--509] presented a framework for an NP optimization problem that searches an optimal value among exponentially-many outcomes of polynomial-time computations. This paper expands his framework to a quantum optimization problem using polynomial-time quantum computations and introduces the notion of an ``universal'' quantum optimization problem similar to a classical ``complete'' optimization problem. We exhibit a canonical quantum optimization problem that is universal for the class of polynomial-time quantum optimization problems. We show in a certain relativized world that all quantum optimization problems cannot be approximated closely by quantum polynomial-time computations. We also study the complexity of quantum optimization problems in connection to well-known complexity classes.Comment: date change

    Analysis of group evolution prediction in complex networks

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    In the world, in which acceptance and the identification with social communities are highly desired, the ability to predict evolution of groups over time appears to be a vital but very complex research problem. Therefore, we propose a new, adaptable, generic and mutli-stage method for Group Evolution Prediction (GEP) in complex networks, that facilitates reasoning about the future states of the recently discovered groups. The precise GEP modularity enabled us to carry out extensive and versatile empirical studies on many real-world complex / social networks to analyze the impact of numerous setups and parameters like time window type and size, group detection method, evolution chain length, prediction models, etc. Additionally, many new predictive features reflecting the group state at a given time have been identified and tested. Some other research problems like enriching learning evolution chains with external data have been analyzed as well

    Analysis of rolling group therapy data using conditionally autoregressive priors

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    Group therapy is a central treatment modality for behavioral health disorders such as alcohol and other drug use (AOD) and depression. Group therapy is often delivered under a rolling (or open) admissions policy, where new clients are continuously enrolled into a group as space permits. Rolling admissions policies result in a complex correlation structure among client outcomes. Despite the ubiquity of rolling admissions in practice, little guidance on the analysis of such data is available. We discuss the limitations of previously proposed approaches in the context of a study that delivered group cognitive behavioral therapy for depression to clients in residential substance abuse treatment. We improve upon previous rolling group analytic approaches by fully modeling the interrelatedness of client depressive symptom scores using a hierarchical Bayesian model that assumes a conditionally autoregressive prior for session-level random effects. We demonstrate improved performance using our method for estimating the variance of model parameters and the enhanced ability to learn about the complex correlation structure among participants in rolling therapy groups. Our approach broadly applies to any group therapy setting where groups have changing client composition. It will lead to more efficient analyses of client-level data and improve the group therapy research community's ability to understand how the dynamics of rolling groups lead to client outcomes.Comment: Published in at http://dx.doi.org/10.1214/10-AOAS434 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org
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